Knitting Theory in STEM Performance Stories: Experiences in Developing a Performance Framework
Bibliographic record
Abstract
Abstract: Gender equality has made its way to the forefront of discussions across various sectors in the Canadian context. Yet the intentional inclusion of gender and other intersectional identity dimensions is just beginning to permeate the realities of performance measurement and evaluation practitioners, particularly those using program theory. There is a vast body of knowledge regarding the measurement of women’s empowerment, gradually declining availability of resources targeting the inclusion of gender in theory, and even less guidance on integrating gender in theory in the context of gendered programming. Similarly, coordinated efforts from multiple sectors have resulted in an abundance of theory regarding girls and women’s representation, recruitment, retention, and promotion within STEM (Science, Technology, Engineering, and Math) but less guidance on the measurement and evaluation in these areas. This article shares recent efforts to bridge the divide using theory knitting to develop a performance measurement framework addressing the decreasing representation of girls and women across the STEM “leaky pipeline” using the COM-B theory of change model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.026 | 0.047 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".